Partial Multi-Label Optimal Margin Distribution Machine

Partial multi-label learning deals with the circumstance in which the ground-truth labels are not directly available but hidden in a candidate label set. Due to the presence of other irrelevant labels, vanilla multi-label learning methods are prone to be misled and fail to generalize well on unseen data, thus how to enable them to get rid of the noisy labels turns to be the core problem of partial multi-label learning. In this paper, we propose the Partial Multi-Label Optimal margin Distribution Machine (PML-ODM), which distinguishs the noisy labels through explicitly optimizing the distribution of ranking margin, and exhibits better generalization performance than minimum margin based counterparts. In addition, we propose a novel feature prototype representation to further enhance the disambiguation ability, and the non-linear kernels can also be applied to promote the generalization performance for linearly inseparable data. Extensive experiments on real-world data sets validates the superiority of our proposed method.

Authors

• Nan Cao
• Teng Zhang Institute of Computing Technology, Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing Key Laboratory of Mobile Computing and Pervasive Device
• Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab, School of Computer Science and Technology, HUST

Keywords

• Machine Learning: Classification
• Machine Learning: Multi-instance; Multi-label; Multi-view learning
• Machine Learning: Weakly Supervised Learning